Adaptive Control Processes

By A. Kakas, David Cohn, Sanjoy Dasgupta, Andrew Barto, Gail A. Carpenter, Stephen Grossberg, Geoffrey I. Webb, Marco Dorigo, Mauro Birattari, H. Toivonen and 9 more. 2010

751 citations recorded by the source corpus.

Subfields: Control and Systems Engineering

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Citations in the corpus

Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 1 Low-Rank Tensor Decompositions (2016, neutral): “The term curse of dimensionality was coined by Bellman (1961) to indicate that the number of samples needed to estimate an arbitrary function with a given level of accuracy grows exponentially with the Full text available at: http://dx.doi.org/10.1561/2200000059 ... number of variables, that is,…”
Dynamic Time Warping Algorithm Review (2008, background): “While first introduced in 60s [1] and extensively explored in 70s by application to the speech recognition [2], [3] it is currently used in many areas: handwriting and online signature matching [4] [5], sign language recognition [6] and gestures recognition [7] [6], data mining and time series clust”
Bayesian Reinforcement Learning (2010, methodology): “Bell-man developed dynamic programing techniques for Bayesian bandit problems (Bell-Nikos man, 1956; Bellman and Kalaba, 1959; Bellman, 1961).”
Control: A perspective (2014, background): “Richard Bellman developed dynamic programming for the optimization of both deterministic as well as stochastic systems, including a foundation for Bayesian adaptive control [58,59,55].”
In Other News: a Bi-style Text-to-speech Model for Synthesizing Newscaster Voice with Limited Data (2019, methodology): “To match the predicted sequence length to the reference sequence length for all comparisons, we use the dynamic time warping (DTW) algorithm (Bellman and Kalaba, 1959).”
Subspace clustering (2012, background): “Historically, the ﬁrst aspect has been described in Ref 10 in the context of optimization problems.”
Recognising realistic emotions and affect in speech: State of the art and lessons learnt from the first challenge (2011, background): “However, they suﬀer from the increasing number of features that leads to regions of the feature space where data is very sparse (‘curse of dimen-sionality’ (Bellman, 1961)).”
Hyperparameter optimization: Foundations, algorithms, best practices, and open challenges (2021, background): “GS suffers directly from the curse of dimensionality (Bellman, 2015), as the required number of evaluations increases exponentially with the number of HPs for a fixed grid resolution.”
Reinforcement Learning based Recommender Systems: A Survey (2021, methodology): “A problem with temporal difference methods, like any tabular RL method, is that they lead to the curse of dimensionality [107].”
From explanations to feature selection: assessing SHAP values as feature selection mechanism (2020, background): “the data becomes a problem since with too many dimensions, every data point in a dataset appears equidistant from the others [7].”
A word at a time: computing word relatedness using temporal semantic analysis (2011, methodology): “The DTW algorithm [5] measures the similarity between two time series that may differ in time scale, but similar in shape.”
On the prediction of claim duration for income protection insurance policyholders (2013, background): “This is commonly known as the curse of dimensionality (Bellman, 1961).”
On manifolds of tensors of fixed TT-rank (2012, background): “To circumvent this “curse of dimensionality” [5], alternative paradigms in their treatment are needed.”
The Nature of Self-Improving Artificial Intelligence (2008, background): “These restrictions lead to well studied decision models known as Markov Decision Processes (MDPs) and Partially Observable Markov Decision Processes (POMDPs) [19, 44, 45, 46].”
Cubature Kalman Filters (2009, background): “Unfortunately, the presently known nonlinear filters mentioned above suffer from the curse of dimensionality [17] or divergence or both.”
this paper (2009, methodology): “We quantify the independence of different component delays with Pearson’s r value [70], dynamic time warping (DTW) [18], and Cramer’s v value [31].”